The roles of STAT1, CASP8, and MYD88 in the care of ischemic stroke

Abstract

Ischemic stroke is caused by blockage of blood vessels in brain, affecting normal function. The roles of Signal Transformer and Activator of Transcription 1 (STAT1), CASP8, and MYD88 in ischemic stroke and its care are unclear. The ischemic stroke datasets GSE16561 and GSE180470 were found from the Gene Expression Omnibus database. Batch effect removal, finding differentially expressed genes (DEGs), weighted gene co-expression network analysis, protein–protein interaction analysis, functional enrichment analysis, immune infiltration analysis, comparative toxicogenomics database analysis were carried out. Gene expression heat maps were drawn, and miRNAs were found that regulate core DEGs. A total of 1183 DEGs were obtained, which were mainly concentrated in immune effector processes, cell activation, and protein serine/threonine kinase activity, the NOD-like receptor signaling pathway, NF-kappa B signaling pathway, C-type lectin receptor signaling pathway, and P53 signaling pathway. Four core genes were identified. Heatmap revealed high expression of (CASP8, MYD88, and STAT1) in whole blood samples of ischemic stroke. Comparative toxicogenomics database analysis demonstrated (CASP8, MYD88, and STAT1) are related to cerebral hemorrhage, reperfusion injury, hypertension, and inflammation. In ischemic stroke, expression of STAT1, CASP8, and MYD88 is higher and leads to poorer prognosis.

1. Introduction

When blood vessels in brain are blocked, brain becomes ischemic and damaged, affecting normal functions, which may lead to ischemic stroke.^[1] Ischemic stroke accounts for 85% of all strokes. There are about 12 million new strokes worldwide every year, most of which are ischemic strokes. With the aging of the population, the global prevalence of ischemic stroke is increasing year by year,^[2] and approximately 100 million people are currently alive due to stroke, many of whom have long-term sequelae. The incidence of ischemic stroke has decreased in affluent countries, but the opposite is true in countries below average, which account for >70% of the global stroke burden. The incidence and mortality rates in Asia and Africa are significantly higher than those in European and American countries.^[3] Ischemic stroke mainly occurs in the elderly population, but the incidence of young people (<50 years old) has also increased in recent years. Men are more likely to develop the disease, but women have a higher risk of mortality and functional disability, especially after menopause.^[4] The clinical manifestations of ischemic stroke can vary depending on individual patient differences and affected brain regions. Common clinical manifestations include hemiplegia or limb weakness, speech disorders, balance and coordination disorders, visual problems, facial expression abnormalities, sensory disorders, and acute headaches.^[5–7] The pathological characteristics of ischemic stroke mainly involve brain tissue damage and blood circulation changes caused by cerebral stenosis or occlusion. The main feature of ischemic stroke is cerebral infarction. After a cerebral infarction, the body will produce an inflammatory response, and excessive inflammation may cause further damage to brain tissue.^[8] Brain tissue damage caused by stroke can have long-term or permanent effects on neurological function, leading to an increase in economic burden. Once ischemic stroke occurs, patients may face a higher risk of recurrence. Despite advances in acute treatment that have significantly reduced early mortality, it remains a difficult problem that cannot be ignored. The recurrence rate is as high as 25% within 5 years, increasing the overall disease burden of patients. Acute medical expenses and long-term rehabilitation care costs related to ischemic stroke cause heavy economic burden to individuals and society. The patient’s ability to work is reduced or completely lost, which has a profound impact on families and society. Acute treatment of ischemic stroke has limitations, and the indications for reperfusion therapy are limited. Intravenous thrombolysis is only suitable for patients within 4.5 hours of onset, and many patients miss the opportunity due to missing the treatment time window. Although the extended time window (<24 hours) provides an opportunity for more patients, mechanical thrombectomy is mainly limited to large-vessel occlusion and covers a limited population. There is a risk of hemorrhagic transformation in thrombolytic therapy, especially in patients with cerebral hemorrhage tendency or a history of anticoagulation therapy. Mechanical thrombectomy may cause vascular injury or distal embolism.^[9–11] Rehabilitation treatment for ischemic stroke is insufficient, individualized treatment is difficult, and the development of new treatments has encountered bottlenecks. Neuroprotective agents although a large number of studies have explored neuroprotective therapies (such as antioxidants and anti-inflammatory drugs), no neuroprotective agent has been successfully tested in large-scale clinical trials. Stem cell therapy and gene therapy are still experimental, and technical and ethical constraints have hindered their clinical translation. Ischemic stroke has significant health, social, and economic impacts worldwide. Although the current treatment has made progress, there are still some problems such as short time window for acute treatment, high risk of recurrence, and limited rehabilitation effect. In the future, comprehensive application of multidisciplinary research results is needed to improve the treatment and prevention strategies of stroke and reduce the global burden of disease.

Bioinformatics makes it easier for us to grasp biological information such as genes and proteins. As sequencing technology matures, we have the conditions to store more data. It also enables numerous scholars to analyze data faster and more accurately, resulting in more reliable results, and has the function of repeated verification.

Medical nursing refers to the process of providing health management, disease prevention, treatment, and rehabilitation services to patients through the skills and knowledge of medical and nursing professionals, in order to enhance patients’ quality of life, and provide support and comfort.

It is still unclear how Signal Transformer and Activator of Transcription 1 (STAT1), CASP8, and MYD88 affect ischemic stroke. We plan to analyze the functions and pathways involved in these genes through bioinformatics and visualize the effects of these genes on ischemic stroke.

2. Methods

2.1. Data sources

The ischemic stroke datasets, GSE16561 and GSE180470, were found from the Gene Expression Omnibus database (http://www.ncbi.nlm.nih.gov/geo/). GSE16561 consists of 39 ischemic stroke cases and 24 normal whole blood samples, while GSE180470 includes 3 ischemic stroke cases and 3 normal whole blood samples.

2.2. Data cleaning

To integrate the datasets GSE16561 and GSE180470, we first merged them using the SilicoMerge package in R software [DOI: 10.1186/1471-2105-13-335]. Next, we applied the removeBatchEffect function from the limma package (version 3.42.2) to eliminate batch effects. After cleaning, the final matrix was generated and used for differential expression analysis to identify differentially expressed genes (DEGs).

2.3. Differentially expressed genes

Differential expression analysis was performed using the “limma” package in R. The original P-values were adjusted using the Benjamin–Hochberg method to control for false discovery rate (FDR). The fold change was also calculated, and DEGs were selected based on an FDR threshold of <.05.

2.4. Weighted gene co-expression network analysis

We performed weighted gene co-expression network analysis (WGCNA) using the GSE16561 and GSE180470 datasets in R. First, we calculated the median absolute deviation for each gene and removed the bottom 50% of genes with the smallest median absolute deviation values. Outlier data were also excluded. We then constructed a gene co-expression network by calculating the correlation between all genes. The power function was used to define the gene relationship, amplifying strongly correlated gene relationships while minimizing the effect of weak correlations. Appropriate parameters were selected to generate the gene connection matrix, which was subsequently converted into a topological overlap matrix to measure gene relationships. Finally, genes with similar expression patterns were clustered into modules.

2.5. PPI network

The DEGs were input into the Search Tool for the Retrieval of Interacting Genes database to obtain protein–protein interaction (PPI) network (confidence level > 0.4). The PPI network was then visualized using Cytoscape software. To identify the most critical genes in the network, we applied the MCC and MNC algorithms and selected the top 20 genes. The final list of core genes was exported for further analysis.

2.6. Functional enrichment analysis

To investigate the biological roles of the DEGs and the associated biological processes, we performed gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. The DEGs were input into the KEGG database to retrieve pathway information. The ClusterProfiler R package was used to identify the most relevant biological processes and pathways associated with the DEGs. GO annotations were performed using the Org.Hs.e.g..db package. We set the significance criteria for the analysis with a P-value threshold of <.05 and a FDR < 0.25. Additionally, we utilized the Metascape database (http://metascape.org/gp/index.html) for further analysis, which provided an efficient platform for viewing and exporting the relevant DEG-related information.

2.7. Gene set enrichment analysis

Gene set enrichment analysis (GSEA) was performed by downloading the GSEA software (version 3.0) from the official website (http://software.broadinstitute.org/gsea/index.jsp). Samples were divided into 2 groups: ischemic stroke and normal controls. The molecular feature data for the analysis was obtained from the Molecular Signatures Database (http://www.gsea-msigdb.org/gsea/downloads.jsp). The c2.cp.kegg.v7.4.symbols.gmt file was used to analyze relevant biological pathways. The gene set size was determined based on gene expression and sample grouping, with a minimum of 5 genes and a maximum of 5000 genes per set. The analysis included 1000 sampling replicates, with a P-value threshold of <.05 and an FDR < 0.25.

2.8. Gene expression heatmap

A heatmap was generated to visualize the expression of core genes in the merged matrix of the GSE16561 and GSE180470 datasets. This heatmap highlights the differences in gene expression between disease and healthy tissues, allowing for a clear comparison of core gene activity across the 2 conditions.

2.9. Immune infiltration analysis

Immune cell infiltration was analyzed using CIBERSORT software (http://CIBERSORT.stanford.edu/), which defines 22 immune cell types. We applied this tool to the matrices of GSE16561 and GSE180470 to estimate the relative abundance of immune cell types in each sample. The analysis utilized linear support vector regression, and we filtered the results by applying a P-value threshold of <.05.

2.10. CTD analysis

To explore the functional implications of the identified genes, we input the gene list into the comparative toxicogenomics database (CTD). This database provides information on diseases most strongly associated with the genes, aiding in the understanding of their roles in various diseases. The results were exported and visualized using Excel, and a radar chart was generated to represent the data.

2.11. miRNA

We used the TargetScan database (www.TargetScan.org) to identify miRNAs that regulate the core genes. By inputting the gene list into the database, we retrieved information about the miRNAs that are likely to interact with these genes, providing further insight into the regulatory mechanisms of the core genes.

3. Result

3.1. Differentially expressed genes

Through gene matrix of GSE16561 and GSE180470, we obtained 1183 DEGs (P < .05) (Fig. 1).

Figure 1.

DEGs analysis. Eighty-three differentially expressed genes were identified. DEGs = differentially expressed genes.

3.2. Functional enrichment analysis

3.2.1. GOKEGG

In GO results, most of DEGs clustered in immune response process, cell activation, protein serine/Threonine kinase (Fig. 2A–C). KEGG result showed, DEGs were almost concentrated in NOD like receptor signaling pathway, NF kappa B signaling pathway, type c lectin receptor signaling pathway, and P53 signaling pathway (Fig. 2D).

Figure 2.

Functional enrichment analysis. (A–C) GO. (D) KEGG analysis. (E–H) GSEA. GO = gene ontology, GSEA = gene set enrichment analysis, KEGG = Kyoto Encyclopedia of Genes and Genomes.

3.2.2. Gene set enrichment analysis

The results of GSEA and GOKEGG were similar, DEGs were mainly clustered in immune response process, cell activation, NOD-like receptor signaling pathway, P53 signaling pathway (Fig. 2E–H).

3.2.3. Metascape

Metascape’s GO enrichment results are mainly NF-κB signaling pathway, NOD-like receptor signaling pathway and MAPK signaling pathway (Fig. 3A). We also use the enrichment term and P-value to visualize the correlation and confidence of the enrichment results (Figs. 3B, C and 4).

Figure 3.

Metascape enrichment analysis. (A) In the enrichment project of Metascape, the NF kappa B signaling pathway, NOD like receptor signaling pathway, and MAPK signaling pathway can be seen in the GO enrichment project. (B and C) Output enrichment networks colored with enrichment terms and P-values. GO = gene ontology.

Figure 4.

Metascape enrichment analysis. The correlation and confidence of each enrichment project.

3.3. Weighted gene co-expression network analysis

Set soft threshold of WGCNA to 6 (Fig. 5A and B). Then, we cluster all genes hierarchically and get 9 important gene modules (Fig. 5C). Analyze the interrelationships between these modules (Fig. 5D). Create a heatmap of correlation between modules and phenotypes (Fig. 6A), present scatter plots of GS and MM correlations of related genes (Fig. 6B and C).

Figure 5.

WGCNA. (A and B) The soft threshold power in WGCNA is set to 6. (C) A hierarchical clustering tree of all genes was constructed, and 9 important modules were generated. (D) The interaction between these modules. WGCNA = weighted gene co-expression network analysis.

Figure 6.

WGCNA. (A) A heatmap of module phenotype correlation. (B and C) A scatter plot of GS and MM correlation for related hub genes. WGCNA = weighted gene co-expression network analysis.

3.4. PPI network

DEGs were constructed into a PPI network using Search Tool for the Retrieval of Interacting Genes (Fig. 7A). Then, import Cytoscape software and use different algorithms to obtain core genes and create Wayne diagrams to obtain intersection genes (Fig. 7B). MCC and MNC algorithms were used respectively (Fig. 7C and D). Finally, we obtained 4 core genes (CASP8, MYD88, STAT1, and TRAF2).

Figure 7.

Construction and analysis of protein–protein interaction (PPI) network. (A) PPI network. (B) Wayne plots to obtain a union as the core genes. (C and D) MCC and MNC algorithms were used to identify core genes.

3.5. Gene expression heatmap

In heatmap results, we found that (CASP8, MYD88, and STAT1) these genes were highly expressed in ischemic stroke samples, but low expressed in normal samples (Fig. 8).

Figure 8.

Gene expression heatmap. Core genes (CASP8, MYD88, and STAT1) were highly expressed in ischemic stroke whole blood samples and low expressed in normal whole blood samples.

3.6. Immune infiltration analysis

With 95% confidence, monocytes and M0 macrophages account for a large proportion in whole gene expression matrix (Fig. 9A). The thermogram of expression level of immune cells showed that the expression level of M0 macrophages was higher in ischemic stroke group and lower in normal group (Fig. 9B). The co-expression pattern of immune cell components shows that monocytes and M0 macrophages are highly expressed at the same time, and the expression of Monocyte and M0 macrophages is positively correlated, which may affect the disease process of ischemic stroke (Fig. 9C).

Figure 9.

Immune infiltration analysis. (A) The proportion of immune cells in the whole gene expression matrix. (B) A heatmap of the expression level of immune cells in the sample. (C) The co-expression pattern diagram of immune cell components.

3.7. Comparative toxicogenomics database

The results provided by the CTD website showed that (CASP8, MYD88, and STAT1) are related to cerebral hemorrhage, reperfusion injury, hypertension and inflammation (Fig. 10).

Figure 10.

CTD analysis. Three core genes (CASP8, MYD88, and STAT1) were found to be associated with cerebral hemorrhage, reperfusion injury, hypertension, and inflammation. CTD = comparative toxicogenomics database.

3.8. miRNAs related to hub genes

We got the miRNA information about miRNAs that regulate core genes (Table 1), and found that the relevant miRNA of CASP8 gene is hsa-miR-455-3p.2; the relevant miRNAs of MYD88 gene are hsa-miR-182-5p; the relevant miRNA of STAT1 gene is hsa-miR-129-5p.

Table 1.

A summary of miRNAs that regulate hub genes.

	Gene	miRNA
1	CASP8	hsa-miR-455-3p.2
2	MYD88	hsa-miR-182-5p
3	STAT1	hsa-miR-129-5p

4. Discussion

Ischemic stroke has serious harm. Cerebral stenosis or occlusion leads to insufficient blood supply, and severe hypoxia and malnutrition will cause brain tissue necrosis, thus leading to life threat. Stroke patients usually require long-term medical care, rehabilitation treatment, and medication treatment. Once ischemic stroke occurs, patients may face the possibility of recurrence. Ischemic stroke includes many complicated pathological processes including ischemic injury, neuroinflammatory response, cell apoptosis, and vascular remodeling. Cerebral ischemia leads to insufficient energy supply and disorder of cell metabolism. This can trigger a series of pathological events, such as intracellular calcium ion imbalance, mitochondrial dysfunction, and oxidative stress. These changes lead to loss of cell Membrane potential, ATP depletion and cell death.^[12,13] Ischemic brain injury can trigger inflammatory responses. Immune cells, as well as various cell types in the brain (such as astrocyte and microvascular endothelial cells), release inflammatory mediators, including cytokines, chemical mediators (such as histamine, complement factors), and inflammatory enzymes. These inflammatory mediators will further promote cell death, neuronal damage and blood–brain barrier destruction.^[14,15] Apoptosis is an important cell death mechanism in ischemic stroke. Ischemic injury can lead to changes in intracellular signal pathways, activate apoptosis related proteins (such as cysteine protease, Bcl-2 family proteins, etc), and thus promote the occurrence of apoptosis. Apoptosis can lead to neuronal death and expansion of lesion areas.^[16–19] After ischemic stroke, in order to restore cerebral blood flow, the machine initiates the process of vascular reconstruction. It is critical for vascular reconstruction. Formation and reconstruction of new blood vessels help restore blood supply and neurotrophic function.^[20,21] A deep understanding of these mechanisms helps to develop better treatment strategies. Abnormalities of STAT1, CASP8, and MYD88 in ischemic stroke may affect the occurrence and development of ischemic stroke.

STAT1 belongs to a member of STAT family. It can regulate cellular signal transduction pathways. STAT1 is a protein of approximately 91 kDa, composed of 2 functional regions: N-terminal signaling domain, C-terminal DNA binding domain. N-terminal domain contains a highly conserved Src homologous region (SH2 region) responsible for phosphorylation and signal transduction of STAT1. C-terminal DNA binding domain is responsible for STAT1 binding to DNA and participating in gene transcriptional regulation. Under the stimulation of extracellular signals such as interferon or other cytokines, STAT1 is phosphorylated to form a dimer. This phosphorylation can be activated by members of the Janus kinase family within the cell. The phosphorylated STAT1 dimer enters the nucleus and binds to specific sequence regions on DNA to regulate the transcription of specific genes. STAT1 plays an important role in transcriptional regulation. Once STAT1 is phosphorylated and activated, it can form transcriptional complexes with other proteins, participating in the regulation of specific genes. STAT1 typically forms complexes with other transcription factors and binds to interferon stimulating response elements to promote the expression of interferon stimulated genes, such as interferon-γ signal pathway.^[22] CASP8, also known as Caspase-8-like apoptosis regulator. CASP8 is a protein composed of 479 amino acids. It contains an N-terminal domain, called Caspase recruitment domain, C-terminal cysteine protease domain. When receiving apoptosis signals (such as TNF)-α. When stimulated by FasL, CASP8 is activated to its active form. Activating CASP8 can further activate other Caspase family members, such as CASP3 and CASP7, through self-cleavage, forming a caspase cascade reaction that ultimately leads to cell apoptosis. CASP8 not only directly participates in the initiation of cell apoptosis, but also regulates the expression and activation of other apoptosis regulatory proteins.^[23] MYD88 (myeloid differentiation primary response 88) is a protein that mediates inflammation and immune response, and is a key member of Toll/IL-1 receptor (TIR) signaling pathway. MYD88 is a protein of approximately 33 kDa, composed of 296 amino acids. It contains an N-terminal TIR domain, C-terminal domain. TIR domain is the functional region of MYD88, responsible for interacting and signaling with other proteins. MYD88 affects activation of extracellular receptors. When receptors such as Toll like receptors, IL-1 receptors, Toll like receptors bind to their ligands, MYD88 interacts with the TIR domain of the receptor to form a signaling complex. This complex further activates downstream signaling pathways, such as nuclear factor-κ B and MAPK pathway.^[24] Some studies have shown that in the neuron Microglia coculture, Minocycline regulates the polarization of M1/M2 Microglia through STAT1/STAT6 pathway to prevent cell death induced by OGD/R of neurons.^[25] Other studies have shown that factors involved in cell apoptosis α and its receptors, FAS, FASL, CASP3, CASP8 β. The expression levels of glycans and DRAK2 are significantly higher.^[26] There are also studies suggesting that MYD88 inhibitors may serve as a small molecule therapy for regulating host immunity to inflammatory diseases and antiviral therapy.^[27] STAT1, CASP8, and MYD88 may affect inflammatory response, cell apoptosis, immune response processes of ischemic stroke.

After ischemic stroke, inflammation, apoptosis and immune regulation are the key links of disease progression and tissue damage, and STAT1, CASP8, and MYD88 are the core molecules related to inflammation and immunity. STAT1 participates in the expression of proinflammatory cytokines in brain tissue and enhances the pro-inflammatory effects of activated microglia and infiltrating immune cells. STAT1 promotes oxidative stress responses and induces neuronal damage and blood–brain barrier disruption. By up-regulating pro-apoptotic genes, STAT1 plays an important role in neuronal apoptosis after stroke. Over-activation of STAT1 may lead to expansion of brain injury and increase of infarct volume. High levels of STAT1 are associated with poor long-term functional recovery and high rates of disability after stroke. Inhibition of STAT1 activity can reduce inflammation and oxidative damage in brain tissue and improve prognosis after stroke. CASP8 is a key molecule in the exogenous apoptosis pathway. After stroke, ischemia and oxidative stress activate death receptors, leading to activation of CASP8 cleavage. Activation of downstream effector molecules triggers neuronal apoptosis. CASP8 inhibits necroptosis process, limiting the transition of cell death forms by degrading RIPK1 and RIPK3. The excessive activation of CASP8 may release more damage-related molecular patterns and further activate the inflammatory response in the brain. Excessive activation of CASP8 can lead to large area of neuronal apoptosis and aggravate the injury of infarct area. Dysregulation of CASP8 may trigger a long-term inflammatory response and inhibit nerve repair. High CASP8 levels are associated with cognitive decline and poor recovery of motor function after stroke. MYD88 is a core regulatory molecule of innate immunity. After stroke, damage-related molecular patterns activate MYD88 through TLRs. Activation of the downstream NF-κB and MAPK signaling pathways promotes the release of inflammatory factors. Myd88-mediated signaling is a major driver of inflammatory responses in the brain after stroke, inducing microglial activation and neutrophil infiltration. MYD88 may disrupt blood–brain barrier, increases risk of brain edema and hemorrhagic transformation. Over-activation of MYD88 aggravated brain tissue damage and delayed lesion repair. MYD88 may be involved in the chronic inflammatory state after stroke and affect the recovery of neurological function. High MYD88 expression is associated with increased mortality and poor functional outcome after stroke. These molecules are key links in the regulation of inflammation and immunity after stroke, and targeted regulation of their activity may become an important strategy.

Nursing care for ischemic stroke requires a comprehensive care plan, including acute phase care, rehabilitation phase care, and measures to prevent recurrence. Continuously monitor vital signs and regularly assess neurological function to track changes in the condition. Monitor blood glucose levels and maintain them within the normal range. Ensure airway patency and provide oxygen therapy or mechanical ventilation support if necessary. Carefully manage blood pressure and use antihypertensive medications as directed by the physician. Avoid large fluctuations in blood pressure to reduce the risk of brain injury. Prevent and treat thrombosis by using antiplatelet or anticoagulant medications as instructed. Watch for signs of bleeding and prevent hemorrhagic complications. Provide appropriate nutritional support, and if necessary, use a nasogastric or gastric tube for nutrition. Offer psychological counseling and support to help patients cope with emotional fluctuations and depression. Encourage patients to participate in social activities to strengthen their social support network. Increase intake of fruits, vegetables, and whole grains for a healthy diet. Take medications on time and follow the doctor’s treatment plan. Provide health education to patients and their families to enhance their understanding of ischemic stroke. Teach stroke prevention methods, such as a balanced diet, regular exercise, and weight control. Through comprehensive care, help ischemic stroke patients restore function, improve quality of life, and prevent stroke recurrence.

5. Limitations and future prospects

The dataset was under representative, and the public database used may have data bias. Future studies should integrate more diverse multicenter clinical and genomic data, and introduce prospective cohort studies to verify the results. The depth of mechanism research is insufficient, and the current studies are mostly based on bioinformatics prediction and verification in published literature, but there are few experimental verification. In particular, the specific signaling pathways and interactions of STAT1, CASP8, and MYD88 in ischemic stroke are still unclear. In the future, more in-depth experimental studies (such as gene knockout models and protein–protein interaction analysis) should be carried out to verify the exact role of these molecules in inflammation, apoptosis and immune regulation after stroke. Although the underlying molecular mechanisms were explored in this study, the lack of validation from clinical studies (such as expression level analysis of relevant proteins in patients’ serum or cerebrospinal fluid) has not directly correlated gene expression with patient functional prognosis. In the future, higher quality clinical trials should be designed to closely integrate laboratory studies with patient data to explore the clinical diagnostic or therapeutic predictive value of STAT1, CASP8, and MYD88 expression levels.

Based on existing studies, targeted therapies targeting STAT1, CASP8, and MYD88 will be developed. STAT1 can reduce inflammation and apoptosis after cerebral ischemia by inhibiting JAK/STAT signaling pathway. In the future, safer and more effective small molecule inhibitors or STAT1-specific antibodies can be developed. The specific inhibitor of CASP8 can be used to reduce neuronal apoptosis after stroke and avoid affecting the protective function of necroptosis. Developing small molecule inhibitors of MYD88, such as ST2825, or blocking the activation of its upstream TLRs, could radically reduce the inflammatory response. Given that STAT1, CASP8, and MYD88 may interact through different pathways, multi-target combined treatment strategies can be explored in the future to comprehensively regulate inflammation, apoptosis and immune response. According to the mechanism of action and dynamic expression characteristics of these molecules, clinical trials can be designed to optimize the time window of drug intervention in the future to achieve maximum therapeutic benefits.

6. Conclusion

STAT1, CASP8, MYD88 provide a certain directional basis for nursing research and the mechanism of ischemic stroke.

Author contributions

Conceptualization: Xiaolu Qin.

Data curation: Shuaimin Li, Xinyu Huang.

Formal analysis: Shuaimin Li, Xinyu Huang.

Visualization: Xiaolu Qin, Shuaimin Li, Xinyu Huang.

Writing – original draft: Xiaolu Qin, Shuaimin Li, Xinyu Huang.

Writing – review & editing: Xiaolu Qin, Shuaimin Li, Xinyu Huang.